Data Classification and Labeling Enforcement
Sensitivity labels get applied correctly when a document is first created inside the right template, but they don't travel reliably after that — a spreadsheet gets exported, copied into a new file, dropped into a shared drive, or built from scratch outside the labeled workflow entirely, and the label either never gets applied or stays stuck at the wrong level. Security teams doing manual spot-checks catch a fraction of this drift, and the gap between what the classification policy says should be labeled and what's actually labeled correctly tends to widen quietly until an audit or an incident surfaces it.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 4-6 hrs/week of manual classification spot-checks and mislabeled-file remediation.
How the automation works
We scan document repositories, shared drives, and collaboration platforms, infer each file's likely sensitivity from its content patterns, and compare that inference against the label the file actually carries. Files with no label, an under-classified label, or a label that no longer matches the content are flagged rather than silently relabeled — each mismatch routes to the relevant data owner with the evidence for the suggested classification, and the owner confirms or corrects it before any label change is applied. This keeps a human in the loop on every reclassification, since automated relabeling can break downstream handling rules tied to the existing label, while still catching the drift that manual spot-checks miss.
Process flow
- 01
Scheduled repository scan trigger
Document repositories, shared drives, and collaboration platforms are scanned on a recurring schedule for classification coverage.
- 02
Infer likely sensitivity from content ai
Each file's content is analyzed against classification patterns to infer its likely sensitivity level, independent of whatever label it currently carries.
- 03
Compare inferred sensitivity to current label ai
Inferred sensitivity is compared against the existing label, flagging unlabeled files and mismatches where the label under- or mis-states the content's actual sensitivity.
- 04
Route mismatches to data owner output
Each flagged file routes to the relevant data owner with the evidence behind the suggested classification, for confirmation or correction.
- 05
Apply owner-confirmed label output
Only after the owner confirms is the label change applied — no file is automatically relabeled on inference alone.
- 06
Report classification coverage output
Coverage and drift trends are reported by repository and department, showing where labeling gaps are concentrated.
Inputs
- Document repository and shared drive access
- Existing sensitivity label taxonomy
- Content classification rules
- Data owner mapping by repository
Outputs
- Unlabeled and mislabeled file report
- Owner confirmation queue
- Applied label change log
- Classification coverage trend reporting
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Auto-relabeling a file without owner confirmation risks breaking downstream automation that keys off the existing label, such as DLP rules or retention policies tied to the current classification — every relabel needs a human to confirm the change is correct, not just plausible.
- Content-based inference produces false positives on structured test data, sample datasets, and documentation that legitimately contains sensitive-looking patterns without being genuinely sensitive, so the confirmation step exists specifically to catch this rather than relabeling on inference alone.
- Sensitive data that gets copied or exported outside the scanned repositories — into personal drives, email attachments, or local downloads — is invisible to a repository scan entirely, so labeling enforcement inside your sanctioned storage doesn't guarantee the copy that left it stays labeled.
- Flooding data owners with low-confidence mismatches trains them to rubber-stamp the queue instead of actually reviewing it, so confidence thresholds and mismatch severity need tuning to keep the review queue genuinely actionable rather than becoming a formality.
Frequently asked questions
Does this automatically change file labels?
No — every suggested reclassification routes to the data owner for confirmation first, since automated relabeling could break downstream handling rules that key off the existing label.
What counts as a mismatch?
A file with no label at all, a label that understates the content's inferred sensitivity, or a label that no longer matches what the file actually contains based on a recent content scan.
Will this catch sensitive data that's been exported outside our repositories?
No — this scans the repositories and drives you connect it to; data copied to personal storage, email, or local downloads outside that scope isn't visible to the scan.
How is this different from DLP alert triage?
This audits and enforces classification labels at rest across your repositories; DLP alert triage handles real-time alerts when data actually moves or leaves a boundary.